Each July (and this year, October), Amazon turns the internet into one big checkout line, and somehow, we all end up holding a cart. This year was no exception: Amazon announced that Prime Day 2025 was its biggest event ever, breaking records in sales and global reach. What began as a mid-summer promotion has evolved into a worldwide retail spectacle that defines how consumers shop and how competitors scramble to keep up. From flash deals to algorithm-fueled recommendations, it’s the ultimate test of how technology, timing, and trust converge to drive digital commerce.

Between July 8 and 11, part one of Amazon Prime Day this year, shoppers spent $24.1 billion online, a 30% increase over last year. And during the sequel, from Oct. 7–8, consumers spent $9.1 billion across US retailers.

Beyond the record numbers and lightning deals, what does Prime Day really tell us about where eCommerce is headed? What can retailers and shoppers learn from this year’s event? Let’s dive into the key takeaways that reveal how Amazon continues to redefine the retail landscape one Prime Day at a time.

6 Key Takeaways From Amazon Prime Day 

1. Voice Search and Conversational AI Are Transforming the Way We Shop

Prime Day made it clear that modern shopping is conversational. Voice search and AI-powered assistants gained significant attention as more consumers used Alexa and Rufus to ask questions and complete purchases without ever touching a screen. 

Roughly 20.5% of people worldwide now use voice search, with more than 8.4 billion voice assistants expected to be active across devices.

Amazon is doubling down on that future with Amazon Nova Sonic, its new model that merges speech understanding and generation into a single, unified system. This integrated approach allows developers to build natural, human-like conversational experiences that respond with remarkable speed and realism, setting a new industry standard for fast, scalable customer interactions.

For retailers, this shift means product data must evolve to reflect how people talk, not just how they type. Attributes, titles, and descriptions should mirror the way shoppers naturally phrase questions, whether it’s “What’s the best wireless speaker for travel?” or “Show me eco-friendly skincare.” Structured and conversationally aware product information — powered by solutions like Akeneo Product Cloud — ensures that every voice query connects customers to the right product, every time.

2. Augmented Reality Becomes the New Standard

What was once a novelty is now an expectation: Augmented Reality (AR) has become a standard part of how consumers explore and evaluate products online. Shoppers are no longer content to imagine how an item will look or fit; they want to see it in their own space, from their own perspective, before they buy. 

Amazon has continued to expand AR features such as “View in Your Room” and virtual try-ons for fashion, furniture, and beauty products, enabling customers to visualize items with impressive realism.

High-quality visuals, 3D models, and detailed metadata now form the foundation of a compelling AR experience. Platforms like Akeneo Product Information Management (PIM) help ensure that this data (from dimensions and materials to accurate color and texture details) is complete and consistent across channels. When paired with immersive technologies, that data transforms into interactive storytelling that builds trust and drives conversion. 

3. Consumers Spent Significant Time Comparing Products

The spending figures from Amazon Prime Day 2025 tell a compelling story: households were buying more, but also buying smarter. 51% of households placed two or more separate orders during the event, and 18% topped $200 in total spend. This pattern of multiple orders and elevated household spend suggests that while the urgency of the deal remains a factor, shoppers are now pacing their purchases and checking back to compare options before committing. This ultimately leads to more confident (and higher-value) decisions.

On Prime Day, even small details like clearer specifications, richer imagery, or transparent delivery information could determine whether a shopper stayed or bounced.

4. Automation Took Center Stage,  From Inventory to Dynamic Pricing

Behind every lightning deal and overnight delivery lies an invisible network of automation. And Prime Day showcased just how advanced that network has become. Sellers leaned heavily on AI-driven systems to forecast demand surges, monitor stock levels, and fine-tune pricing dynamically as trends changed throughout the event. An analysis of Amazon Prime Day 2025 emphasized that automation and AI were the backbone of the event’s success. It noted that Amazon’s vast logistics and supply-chain operations relied on predictive demand forecasting, real-time inventory management, and fulfillment automation to handle record order volumes efficiently. In short, AI-driven automation was powering Prime Day.

For Amazon, this sophistication translates into unprecedented efficiency. Algorithms constantly balance consumer demand and fulfillment capacity to keep both prices and availability optimized. For sellers, automation ensures that popular products stay in stock and are priced competitively without endless human oversight. It’s the ultimate balancing act: maximizing profit margins while delivering value at the speed of modern commerce.

But automation is also helping drive sustainability behind the scenes. By optimizing routes and inventory management, the same systems that accelerate fulfillment are also minimizing waste and improving energy efficiency, showing that innovation and environmental responsibility increasingly work hand in hand.

5. Marketplace Expansion Is Unlocking New Opportunities

And speaking of marketplaces, Amazon’s growth in 2025 made its momentum impossible to ignore. The launch of the Amazon Ireland store marked its 23rd marketplace worldwide, expanding access to millions of new customers and reinforcing Amazon’s dominance in Europe. For sellers, this expansion brings opportunity, but also complexity. Reaching new audiences means adapting to new languages, currencies, tax systems, and cultural expectations, all without losing brand consistency.

International expansion now demands a meticulous approach to product information. What resonates with a U.S. customer might not appeal to a shopper in Dublin or Düsseldorf. Localization is more than translation; it’s about context, tone, and cultural nuance. From product names to sizing charts (and even color descriptions!), every detail matters.

This is where structured data management becomes mission-critical. With tools like Akeneo PIM and Akeneo Activation, brands can syndicate accurate, localized content to every marketplace while maintaining a unified brand identity. 

6. Sustainability Is Becoming a Key Purchase Driver

Sustainability has become a deciding factor in how people shop. As consumers grow more environmentally conscious, programs like Amazon’s Climate Pledge Friendly have gained real traction. The initiative, which highlights products certified for meeting sustainability standards such as compact packaging, energy efficiency, or carbon-neutral production, continues to shape buyer preferences across categories. Shoppers are now scanning for that green badge before clicking “Add to Cart,” signaling that sustainability has moved from idealism to expectation.

According to Amazon, millions of products now carry the Climate Pledge Friendly label, and participating brands have seen measurable boosts in visibility and conversion. 

Transparency also translates directly into loyalty and spend: nearly 42% of consumers are willing to pay 24% more when a brand clearly communicates its values. Environmental impact and product lifecycle transparency are increasingly influencing where and how people spend their money.

For brands, meeting these expectations starts with accurate and trustworthy data. Solutions like Akeneo Supplier Data Management (SDM) and Akeneo PIM empower businesses to collect and enrich sustainability-related information (from material sourcing and packaging details to verified certifications) directly from their suppliers. Together, SDM and PIM create a single source of truth for sustainability data, enabling brands to communicate their environmental impact clearly and confidently across every channel. With this level of visibility and control, brands can back up their claims and meet marketplace requirements like Amazon’s Climate Pledge Friendly program.

The Commerce Lessons Behind Prime Day

Amazon Prime Day 2025 was another glimpse into the future of digital commerce. From conversational AI to automation, AR, and sustainability, every trend pointed to the same truth: data now drives every decision, interaction, and experience. And brands will thrive by treating product information as a strategic asset, one that powers discovery and conversion, rather than an afterthought.

With solutions like Akeneo Product Cloud, businesses can turn scattered data into actionable insight, ensuring every product, in every channel, tells a consistent and credible story. As commerce continues to change, the most successful retailers will anticipate them through better data and a deeper understanding of what their customers truly value.

Love it or hate it, AI has an impact on revenue.

PwC predicts that AI technology could generate $15.7 trillion in revenue by 2030, boosting the GDP of local economies by an additional 26%. And a study we conducted here at Akeneo found that two-fifths (40%) of consumers were willing to pay an average of 25% more for a more personalized, tailored experience powered by AI.

At peak moments, AI-assisted experiences are now moving the market at scale: during the 2024 holiday period, AI-influenced online sales reached $229B globally as shoppers interacted far more with chatbots and AI assistants. Amazon recently announced that it expects its AI shopping assistant, Rufus, to generate an extra $10 billion in sales this upcoming holiday season.

And of course, LLM-powered search and shopping is only continuing to grow in popularity as it slowly poaches searchers from more traditional methods; AI overviews can reduce click-throughs from traditional search results by anywhere from 15% to 64%, depending on the industry and search type, and 60% of searches now terminate without users clicking through to another site. 

 Now, it’s pretty obvious how this transition from traditional search and shopping experiences to AI-powered ones can affect customer-facing teams like marketing, eCommerce, and product teams: 56% of marketers say their company is taking an active role in implementing and using AI, and 73% say AI plays a role in creating personalized customer experiences.

But there’s a quieter, equally critical shift happening behind the scenes: IT departments are quickly becoming the gatekeepers of AI readiness.

The Impact of AI Commerce on IT Teams

In a commerce environment where speed and relevance drive conversions, poor data quality is a direct threat to revenue. Gartner estimates poor data quality costs organizations an average of $12.9 million per year, while Forrester finds that fragmented data directly contributes to lost revenue, slower time to market, and higher return rates.

As AI-powered search becomes standard, IT leaders are under mounting pressure to adapt their tech stacks to meet an entirely new set of requirements. 

Traditional automation is built on preprogrammed workflows, but in the age of intent-driven search and AI search agents, IT teams must design systems that allow AI agents to sense, decide, and act dynamically based on changing conditions, often without waiting for human approval.

This requires an architecture capable of supporting real-time data integration across core systems like ERP, PIM, OMS, DAM, and CDP, ensuring that AI tools have immediate access to accurate information. It also demands seamless interoperability, so AI can call APIs for pricing, content, or fulfillment data whenever needed, without bottlenecks. 

Just as importantly, these systems need feedback loops that enable AI agents to learn from results, refine their decision-making, and improve over time. Without these elements in place, AI simply can’t deliver on its promise of more intelligent, adaptive commerce experiences.

But meeting these requirements comes with challenges. IT leaders must avoid the costly trap of duplicate tech investments as AI capabilities spread across multiple platforms, and they must keep system sprawl and operating costs under control in order to maintain performance and manageability as stacks grow more complex. 

With MIT recently announcing that 95% of GenAI pilot programs fail and Gartner reporting that over 40% of current agentic projects will stall out before 2027, it’s become more important than ever before to ensure that these AI investments are successful, but the question still remains; how?

How IT Can Ensure AI Success

Here’s the harsh reality: while ERPs and MDMs play essential roles in managing operational and generalized data, they weren’t designed to handle the kind of rich, contextual product information that powers revenue and fuels AI.

This is where Product Information Management (PIM) comes in.

Think of PIM as the end-to-end supply chain for product data. Just as supply chains ensure raw materials get transformed into finished goods and delivered to customers, PIM ensures product information is enriched, organized, and distributed wherever it’s needed.

This matters because AI can only work with the information it’s given. A large language model or generative search tool isn’t magic; it can’t invent accurate specs or infer missing details. To interpret buyer intent and deliver relevant recommendations, these tools need structured, complete, and contextualized product data. 

For IT leaders, PIM directly addresses the core priorities of integration, governance, and scalability. 

Integration

On the integration front, modern PIM systems are built API-first, making it easy to connect with ERPs, MDMs, DAMs, eCommerce platforms, and increasingly AI engines. Instead of custom workarounds or brittle connectors, IT departments gain a flexible, future-proof hub that allows product data to flow where it’s needed with a PIM.

Governance

Governance is another critical advantage with PIM as organizations can establish clear workflows and validation rules to ensure that data is complete, accurate, and compliant with internal standards and external regulations. This reduces the risk of bad data reaching buyers (or worse, regulators) and builds the trust that’s essential when AI is making recommendations on a company’s behalf; as Gartner predicts, enterprises using AI governance platforms will enjoy significantly higher customer trust ratings in the years ahead.

Scalability

Finally, there’s scalability. AI-powered commerce is evolving quickly, and businesses need tech stacks that can adapt just as fast. A PIM system offers the agility to expand into new channels, markets, or business models without rebuilding the entire data infrastructure each time. Whether it’s supporting a new conversational AI interface, integrating with a marketplace, or enabling real-time product updates, PIM provides the flexible foundation IT leaders need to scale with confidence.

Ultimately, investing in a PIM gives IT leaders and their organizations the agility to adapt quickly, the governance to ensure accuracy and compliance, and the integration layer needed to connect every tool in the stack. Most importantly, it positions businesses to fully leverage AI commerce tools and deliver the kind of seamless, trustworthy product experiences that today’s buyers expect.

IT As the Gatekeepers for AI-Driven Revenue

The interface of commerce is shifting from search bars and storefronts to intelligent, adaptive systems that work on behalf of the customer.

At the heart of this transformation are IT teams. Marketing, product, and commerce leaders may shape the customer-facing experiences, but those experiences are only as strong as the infrastructure behind them. 

IT holds the keys to making AI commerce possible: ensuring real-time data flows, connecting systems through APIs, governing information for trust and compliance, and maintaining the agility to adapt as technologies evolve.

The path forward for IT leaders is clear: embrace openness, centralization, governance, and continuous evolution. By doing so, they will not only keep their organizations competitive but also help define what AI-powered commerce looks like in the years ahead. 

In case you missed it, we shared some very exciting news last week as part of our 2025 Autumn Release: the launch of Akeneo DAM!

For years, our customers have trusted Akeneo to help them manage, enrich, and distribute product information seamlessly across channels. Now, with Akeneo DAM, we’re taking that vision one step further, bringing digital asset management directly into the Akeneo Product Cloud.

But what exactly does Akeneo DAM do? And more importantly, how can it help your teams work smarter, faster, and more creatively? Let’s take a closer look.

Challenges of Asset Management

Today’s commerce teams face a growing list of pressures. There’s constant demand to do more with less; launching products faster, expanding to new channels, and creating content that converts, all while budgets tighten and headcount stays the same.

Digital assets are at the heart of those challenges. Images, videos, PDFs, 3D renders, and marketing visuals power every customer touchpoint, from product pages to ads to in-store displays. But managing them efficiently is another story.

Finding the right asset can feel like searching for a needle in a haystack, especially when assets are scattered across shared drives, cloud folders, and disconnected tools. Even once the right file is located, preparing it for each channel (resizing, cropping, adjusting backgrounds, creating locale-specific variations, etc.) can eat up hours of manual work.

At the same time, organizations are under pressure to prove ROI on every technology investment. Maintaining multiple systems and integrations drains IT resources, while the learning curve for new tools slows team adoption. Jumping between platforms to connect product data, digital assets, and marketing content is inefficient and frustrating.

And here’s the reality: today’s customers won’t wait. If product pages or visuals don’t load quickly, they move on. Teams need to deliver engaging, on-brand assets instantly, everywhere their customers shop, all while facing shrinking budgets and expanding channel requirements.

Introducing Akeneo DAM: One Seamless Workflow for Products and Assets

That’s where Akeneo DAM comes in. Built natively within the Akeneo Product Cloud, it gives you a single, unified workflow for managing both product information and digital assets without the headaches of multiple tools, integrations, or extra maintenance.

With Akeneo DAM, you can prepare and send assets to market alongside product information in one streamlined process. There’s no need for additional connectors, middleware, or external systems. Everything lives and works together harmoniously in one place.

Standalone DAMs offer robust, enterprise-level functionalities, and are probably the right solution for larger organizations with complex digital asset management needs. But Akeneo DAM offers a simple, straightforward solution for those organizations who want to associate digital assets directly with products already in Akeneo PIM, allowing their teams to manage and enrich both product data and digital assets in tandem. You can define relationships, assign attributes, localize for different locales or language variations, and ensure every image, video, and document supports the story your product tells.

Akeneo DAM also comes with advanced metadata management and AI-powered asset tagging that makes organizing and retrieving assets effortless. Each file is enriched with context and linked to product attributes, categories, and usage guidelines so that you can find what you need when you need it.

And don’t fret – if your organization already has a corporate DAM, Akeneo DAM can serve as the perfect bridge. Corporate DAMs are great for housing brand and campaign assets, but they often lack visibility into product details or channel-specific needs. Akeneo DAM fills that gap by connecting creative assets to the real-world context of your product catalog, order, and hierarchy.

In short, Akeneo DAM brings speed, consistency, and simplicity to how you manage and deliver digital assets so your teams can focus less on juggling systems and more on creating experiences that convert.

Akeneo DAM Feature Highlights

1. Automated AI Asset Tagging

Finding and using assets has never been easier. With AI-driven asset tagging, Akeneo DAM automatically applies consistent, descriptive keywords to every file in your library. That means your teams can quickly locate the right assets, ensure alignment across campaigns, and maintain a cohesive content library without hours of manual organization.

By improving asset visibility, you not only save time but also enhance searchability and SEO, helping customers discover your products faster through more accurate and relevant keywords.

Less time spent organizing means more time creating and launching beautiful, on-brand product experiences.

2. Automated AI Asset Transformation

Creating multiple versions of an image for different channels is typically a tedious, manual process, but with Automated AI Asset Transformations, Akeneo DAM can automatically resize, crop, and remove backgrounds from images, applying intelligent editing that adapts each asset to your defined channel requirements.

Teams can scale transformations effortlessly across entire product families using pre-defined operations. This ensures every visual fits perfectly, whether it’s a mobile app thumbnail, a high-resolution print image, or a marketplace listing photo.

The result? Consistency and market relevance at scale, all without leaving Akeneo.

3. Global Content Delivery Network

Speed matters, both for your team and your customers. Akeneo DAM’s Global CDN ensures that product assets are delivered faster and more reliably across every touchpoint. By leveraging a global network of local servers, assets load quickly no matter where your customers are, reducing bandwidth usage and bounce rates.

Even better, assets are delivered directly from Akeneo Product Cloud, so there’s no need for extra software, infrastructure, or middleware. That means lower costs, faster page load times, and a smoother path from creation to conversion.

It’s another way Akeneo DAM helps teams go from “ready” to “live” in record time.

The Power of Akeneo DAM + Akeneo PIM

When you combine Akeneo DAM with Akeneo PIM, you unlock a whole new level of efficiency and performance. Here’s how this powerful duo transforms your go-to-market process:

• Deliver compelling product stories: Great product experiences aren’t just about accurate specs. By pairing product information with high-quality, context-rich assets, brands can increase conversions and build trust with customers. Akeneo DAM helps teams deliver immersive product stories that blend data and visuals seamlessly.
• Get to market faster: Time is money, especially in retail. With both product data and assets managed in Akeneo, teams can prepare and launch products faster, eliminating manual steps between departments and systems.
• Boost efficiency and focus: Gone are the days of toggling between five different tools to get one product ready for launch. With everything in one place, teams work smarter, reduce duplication, and keep their creative energy focused where it matters most.
• Reduce total cost of ownership: By consolidating tools, you also reduce your total cost of ownership. There’s no need for extra IT resources, and no middleware to maintain. It’s one system, one workflow, and one source of truth.

The Future of Product Experiences Starts Here

In a world where every click, swipe, and scroll shapes how customers perceive your brand, the right product experience can make all the difference. That experience depends not only on accurate data but also on engaging visuals, and bringing those two elements together shouldn’t be complicated.

Akeneo DAM was built to simplify that process, helping teams to manage, connect, and deliver digital assets as part of a cohesive product story. By uniting product information and assets in one platform, Akeneo DAM helps teams work faster, collaborate smarter, and launch stronger without compromise.

So whether you’re looking to streamline workflows, cut costs, or simply get your products to market faster, Akeneo DAM is here to help you do it all beautifully, efficiently, and seamlessly.

Ready to see Akeneo DAM in action?

Visit our Autumn Release page to learn more, or register for our live Deminar on December 3 to discover how your team can create richer, more efficient product experiences today.

Manufacturing isn’t what it used to be. The factory floor has traded in clipboards and constant machine checks for smarter systems that never take a break. 

In fact, 48% of manufacturers now use AI-driven predictive maintenance to stop breakdowns before they occur.

Artificial Intelligence has become a powerful engine for progress, taking over the repetitive tasks humans were never eager to do. While the more obvious applications of AI tend to be in the customer-facing areas like marketing and eCommerce, it can also work in the background to quietly keep production lines moving and the manufacturing industry evolving at full speed.

What Is Artificial Intelligence In Manufacturing?

AI in manufacturing refers to the use of AI algorithms, machine learning, and other AI systems to support and enhance every stage of the manufacturing process. Unlike traditional automation, which follows rigid rules, AI can process large and complex streams of information, adapt in real time, and uncover insights that static systems miss.

At its core, AI in manufacturing is not about replacing the human element but about adding an intelligent layer to the systems already in place. AI models are built to recognize patterns and handle volumes of information far beyond the reach of traditional tools. The result is a manufacturing process that is more adaptive and resilient. 

Unlike older systems bound by rigid instructions, AI introduces flexibility and continuous learning that gives manufacturers the ability to stay strong as customer expectations and technologies continue to advance.

The Impact AI Has On the Manufacturing Industry

Increased Efficiency

AI is transforming efficiency in the manufacturing industry through predictive maintenance and automation that reduce downtime and streamline production lines. With predictive maintenance, AI algorithms track the health of machines in real time, detecting early warning signs of failure and allowing scheduled repairs before breakdowns occur. 

A great example of this is Siemens MindSphere, an industrial IoT and AI-powered platform that connects factory equipment, collects sensor data, and predicts failures before they happen. By recommending optimal repair schedules and monitoring asset performance, MindSphere helps manufacturers avoid costly disruptions while maintaining peak efficiency.

Yet efficiency in manufacturing isn’t limited to machine health alone. AI systems also automate repetitive tasks that once consumed valuable human time, freeing workers to concentrate on more strategic responsibilities. From adjusting equipment settings on the fly to optimizing processes, AI applications ensure every stage of the manufacturing process runs at peak performance. The outcome is a faster operation that can adapt to changing demands without missing a beat!

Improved Safety Standards

AI-driven monitoring systems can oversee factory environments in real time, spotting hazards like overheating machinery, chemical leaks, or unsafe behaviors. By alerting teams early, these systems prevent accidents before they escalate, keeping people and operations out of harm’s way.

IBM Maximo Visual Inspection, an AI-powered tool within the IBM Maximo Application Suite (MAS), is a good example of these improved safety standards in practice. It uses computer vision to detect and identify unsafe conditions on the factory floor. Paired with predictive analysis, solutions like this give manufacturers early warnings, strengthen compliance with safety standards, and create safer working environments.

Beyond immediate detection, AI models process data from sensors and historical incidents to predict risks and enforce safety standards. Whether it’s forecasting equipment fatigue or identifying hazardous conditions, AI applications reinforce a culture of safety that reduces liability and creates a more resilient workplace for everyone.

Creation of Digital Twins

Digital twin technology is the practice of creating a digital replica (or “twin”) of a physical asset, machine, or process. This virtual model is connected to real-world equipment through sensors and IoT devices, allowing it to mirror performance in real time. Because it continuously receives live data, the digital twin behaves just like the physical object it represents, making it a powerful tool for experimentation and insight.

With this approach, manufacturers can test scenarios and analyze data in a risk-free digital environment before applying changes to the real-world system. A factory could model how a new process would affect output or stress-test a machine virtually to uncover potential weaknesses in order to streamline the process and prevent costly mistakes that come with testing in live production.

Digital twins become even more powerful when paired with AI. While the twin provides the virtual model, AI algorithms predict outcomes and continuously refine simulations. This turns digital twins from static replicas into AI-driven systems that can optimize workflows and even suggest improvements for sustainability!

We can look to BMW’s iFactory strategy as an example of this concept in action, which uses digital twins enhanced with AI to design, test, and optimize production systems virtually before they’re rolled out on the shop floor. By creating a complete digital mirror of its factories, BMW can fine-tune workflows and ensure sustainability goals are met, all without disrupting real-world production.

Enhanced Design

When it comes to design, AI adds both intelligence and creativity to the process! AI systems sift through vast datasets (such as customer preferences and market trends) to suggest improvements for product designs. Instead of relying solely on intuition, engineers gain data-driven insights that accelerate innovation and reduce the risks of trial-and-error development.

A strong example of this in the manufacturing industry is Autodesk Generative Design, which uses AI algorithms to explore countless design possibilities based on performance goals and constraints, which enables manufacturers to quickly identify optimized designs that are stronger while using less material.

Beyond optimization, AI also speeds up the entire design process by transforming raw ideas into viable prototypes at record speed and enhancing functionality while leaving less room for waste! Ultimately, AI-driven design enhances human creativity and streamlines the process of transforming concepts into solutions that are ready for production more efficiently than ever.

Simplified Supply Chain and Inventory

The supply chain is one of the most complex aspects of the manufacturing industry, and AI is proving invaluable in making it more predictable and resilient. AI solutions powered by machine learning help manufacturers forecast demand with greater accuracy, allowing them to optimize inventory levels and avoid both shortages and overstock.

Beyond efficiency, AI introduces a new level of transparency. Manufacturers can now monitor every stage of the manufacturing process, ensuring compliance while identifying and addressing bottlenecks in real time. This is especially crucial as Digital Product Passport (DPP) legislation goes into effect in 2026 and requires an unprecedented level of supply chain transparency and collaboration.

A solution like Akeneo’s Supplier Data Manager (SDM) can be very helpful when it comes to collecting and optimizing product data collected throughout the supply chain. SDM replaces outdated, manual processes with AI-driven automation that streamlines supplier data exchange and enrichment processes. By using shared templates and flexible workflows, suppliers can onboard product data at scale while manufacturers are able to ensure accuracy and consistency before bringing the data into Akeneo PIM.

Quality Control

Quality control is one of the areas where AI is making the most immediate impact. Traditional inspection methods often rely on human oversight and manual checks, which can miss subtle flaws.

A great example here is LandingLens by Landing AI, which is a tool that uses computer vision to inspect products in real time and catch defects that might otherwise go unnoticed. By reducing recalls and improving consistency, tools like this help manufacturers deliver excellent products at scale and ensure their quality is reliable at every stage of the manufacturing process.

Sustainability

With nearly half of all consumers willing to pay 25% more if a company were to clearly communicate sustainability practices, it’s no wonder that more and more manufacturers and merchants are looking to collect, track, and communicate this information. 

From reducing material waste to lowering energy consumption, AI-driven systems improve production schedules and resource use in ways that lessen environmental impact without sacrificing performance. A strong example is Microsoft AI for Sustainability, which provides real-time insights into energy use and emissions, enabling manufacturers to track their footprint and take action.

The benefits go beyond compliance. AI enables manufacturing processes that are both efficient and responsible, reducing costs while aligning with regulations and consumer expectations for greener practices. Embedding AI into daily operations allows manufacturers to position themselves as both innovative and responsible, proving that progress and sustainability can go hand in hand.

AI Is Redefining Manufacturing

From predictive maintenance and digital twins to supply chain resilience, AI is reshaping every corner of the manufacturing process. And with platforms like SDM, manufacturers can extend that intelligence beyond the factory floor into their product data, ensuring efficiency and accuracy.

The future of manufacturing lies in the hands of companies that embrace AI as a strategic partner. Those who invest today will be best positioned to deliver consistent product experiences and lead confidently into the next era of commerce.

Shopping today feels less like a straight line and more like a maze, one that consumers navigate with a tap, a scroll, and a swipe. They jump between apps, stores, and social feeds without missing a beat, expecting every stop along the way to feel connected and effortless. Their expectations are rewriting the rules of engagement for modern commerce.

According to our recent B2C survey, general and specialty retail stores and online marketplace websites emerged as the most common shopping channels. Even after years of eCommerce acceleration, shoppers haven’t abandoned the thrill of in-store experiences. The chance to see, touch, and test products still matters! Rather than choosing one over the other, consumers are blending both worlds, creating a commerce landscape where digital discovery and physical connection go hand in hand.

For businesses, the challenge is keeping up with this rhythm while maintaining consistency and speed across every touchpoint. Data, technology, and teamwork have become the glue holding it all together. 

Let’s take a closer look at the omnichannel trends redefining how consumers shop and how businesses sell, and how these changes are setting the pace for growth in 2025 and beyond.

What Is Omnichannel Commerce?

Omnichannel commerce means selling products across multiple channels while maintaining a unified customer experience. It brings together every touchpoint where customers interact with a brand, merging online platforms and physical stores into a single strategy. At its core, it’s about creating a seamless shopping journey that delivers personalization and consistent interaction at every stage.

8 Key Omnichannel Shopping Trends To Learn From 

If you want to understand where retail is headed, don’t just ask the brands, ask the shoppers. Consumers are already rewriting the rules of omnichannel retail, blending online, offline, and everything in between into their own dynamic shopping journeys.

From the channels they trust to the frustrations they voice, these trends reveal exactly what today’s buyers expect, and where retail businesses need to step up:

1. Customers Expect AI to Make Shopping More Personal

Shoppers today expect brands to know them, not just recognize them. Generative AI is transforming how customers discover and engage with products across channels. Consumers now rely on AI-powered tools for personalized recommendations, chatbot-assisted shopping, and dynamic search that surfaces the most relevant results. For many, it’s no longer about scrolling through endless product listings but instead letting an AI assistant handle the search and surface the right products that match their taste and budget instantly. In fact, 49% of global shoppers say AI-powered recommendations have directly influenced their purchase decisions, proving just how deeply these technologies now shape buying behavior.

For businesses, this transition represents a turning point in digital engagement. Generative AI helps brands create content at scale, from automated product descriptions to localized campaigns that resonate across regions. Retailers use AI to refine their personalization strategies and even predict purchasing behavior. But success depends on the quality of product data that fuels these algorithms: clean, complete, and clear information ensures that what AI delivers feels human and accurate. The future of omnichannel personalization lies in data precision and agility, where every recommendation reflects a brand’s understanding of its customer.

2. Shoppers Blend Convenience and Connection Through BOPIS & BORIS

Customers have embraced the best of both worlds. They want the convenience of digital shopping and the immediacy of physical pickup; and increasingly, they expect brands to offer both without compromise. “Buy Online, Pick Up In-Store” (BOPIS) and “Buy Online, Return In-Store” (BORIS) have become fundamental behaviors for modern shoppers. They like being able to browse from anywhere, secure an item in minutes, and either pick it up the same day or return it easily in person.

From a customer perspective, these options remove uncertainty and reduce waiting time. They make online shopping feel tangible and trustworthy. Shoppers also value the sense of control, being able to see and touch the product before finalizing their purchase or refund gives reassurance that pure eCommerce can’t match!

For retailers, however, this convenience comes with new operational complexity. Synchronizing product availability, pricing, and descriptions across both online and in-store systems is notoriously difficult. One mismatch can quickly lead to frustration and lost trust.

Akeneo Activation helps brands bridge the gap between systems and channels by automating the flow of accurate product information in real time. It eliminates manual work by keeping channel requirements up to date and ensuring that every product listing meets the unique specifications of each marketplace or retailer. With Activation, teams can launch new products faster, optimize live product detail pages (PDPs), and maintain cohesive visuals and descriptions — so every touchpoint delivers a unified experience for the customer!

3. Shoppers Research Online Before Buying In-Store

Consumers have become meticulous researchers. The Research Online, Buy Offline (ROBO) trend is proof that digital and physical shopping complement each other. In fact, the trend is stronger than ever, with up to 76% of consumers using multiple channels and touchpoints to inform their purchase decisions. Shoppers rely on online research to guide offline buying, checking specs, reading reviews, and watching influencer demos before ever setting foot in a store. They arrive ready to purchase instead of browse, turning in-person visits into the final step of an already digital decision.

For businesses, this behavior demands strong online content and absolute consistency across touchpoints. A detailed, genuine digital presence builds confidence and drives real-world conversions. Retailers that treat their online channels as digital showrooms (providing clear imagery and precise information) strengthen both their eCommerce and physical store performance.

4. Consumers Use AI Search to Find and Commit Faster

The rise of conversational search is transforming how shoppers discover and evaluate products. Consumers are no longer typing stiff keywords but are now asking natural, open-ended questions like “What’s the best cruelty-free moisturizer under $30?” or “Which headphones are most comfortable for long flights?” and expecting correct, personalized results instantly. It highlights a growing expectation for search tools that can interpret meaning and context, rather than simply respond to literal keywords.

AI-powered search represents a major leap from traditional keyword matching to intent-based discovery. Using machine learning and natural language processing (NLP), these systems can interpret what a shopper means, not just what they say. Rather than relying solely on static tags or product rules, AI search learns from user behavior and adapts accordingly. For example, if someone searches “best trail running shoes for wet conditions,” a standard search engine might list every product tagged “trail” or “running,” leaving shoppers to filter through irrelevant results. An AI-powered search, on the other hand, analyzes reviews, purchase data, and location trends to surface waterproof running shoes that perform well in rainy climates. It looks for products that truly align with the user’s intent!

As AI-driven commerce evolves, visibility now depends on how well products are understood by intelligent search systems. AI Discovery Optimization, a recent capability within Akeneo PX Insights, helps brands analyze how their products are discovered — or missed — across AI-powered shopping experiences. It surfaces insights on natural language and AI-assisted search behavior, highlights gaps in visibility and relevance, and identifies opportunities to enrich product data so it better reflects evolving consumer intent. By optimizing product content for emerging AI discovery channels, businesses can ensure they stand out with precision and clarity at every key point in the shopping journey.

5. B2B Buyers Mirror Consumer Shopping Behaviors

B2B buyers are no longer satisfied with outdated portals or clunky catalog PDFs. They expect the same simplicity and agility they experience as consumers, complete with personalized pricing and omnichannel consistency. In fact, 84% of B2B buyers say it’s important for sellers to provide a seamless experience across multiple channels, and 73% already prefer digital channels such as marketplaces and mobile apps as part of their purchasing process. This evolution shows that B2B commerce is no longer trailing behind B2C: it’s catching up fast.

For B2B sellers, this shift means elevating the buying experience to meet rising expectations. That involves managing multiple catalogs and configurations while ensuring that every piece of product information remains synchronized across systems and channels. Akeneo Shared Catalogs provides the foundation for this transformation by simplifying how product information is shared and managed. It enables businesses to customize and securely distribute up-to-date product data directly through a centralized online portal. Automatic synchronization with Akeneo PIM ensures that every catalog remains current and consistent, giving distributors, partners, and sales teams instant access to the information they need — without manual updates or IT involvement!

For B2B sellers, this means more than faster launches. Akeneo Shared Catalogs enables seamless collaboration and strengthens trust across partner networks. With Shared Catalogs, every stakeholder can confidently access the latest product details, pricing, and assets in real time, removing inconsistencies and minimizing inaccuracies. The result is a more transparent B2B buying experience, one that deepens relationships and transforms reliable product data into a true competitive advantage.

6. Shoppers Want Flexible Returns Without the Friction

Online shoppers have grown accustomed to flexible return policies, and they’re not giving them up anytime soon. But while customers love the convenience, returns are increasingly cutting into profits. In many cases, they’re the direct result of (you might have guessed it!) poor product data. Globally, around 40–50% of consumers returned a product in the past year due to incorrect or misleading product information. Returns create operational headaches for retailers and frustration for customers, often stemming from fragmented product data across channels.

Customer expectations around returns have never been higher. 38% expect free delivery, 33% expect a free return process, and another 28% prioritize an easy return experience. These expectations have become essential parts of what customers consider good service, not optional perks. Retailers that fail to meet them risk damaging loyalty and driving shoppers to competitors who make convenience a core part of their promise.

To adapt, businesses are turning to AI-powered tools and richer product content to reduce return rates and rebuild buyer confidence. By using AI to effortlessly generate, enrich, and translate product information, tailored to their brand voice, retailers can create more accurate and complete product experiences. Akeneo PIM makes this possible by centralizing and enriching product data so every channel displays up-to-date information. When product details are clear, shoppers know exactly what to expect, which leads to fewer surprises and fewer returns!

7. Traditional Search Still Matters in an AI-Driven World

Despite the rise of AI-powered discovery, traditional search remains a cornerstone of how consumers find products online. When it comes to online search and discovery, 26% of consumers say they’re most likely to use traditional search engines, while 22% turn to online marketplaces. These channels continue to dominate the early stages of the shopping journey, serving as the starting point for product research and validation.

For brands, this means that SEO and structured product data are still essential parts of an omnichannel strategy. Optimizing for both traditional and AI-driven discovery ensures that products appear wherever consumers begin their search, whether that’s Google, Amazon, or a generative AI assistant. The brands that maintain visibility across both established and emerging discovery channels are the ones that stay top of mind and top of search results.

8. Gen Z Is Defining What Omnichannel Really Means

Gen Z’s habits and demands are powering the transformation of the retail sector. As digital natives, they move fluidly across apps, devices, and physical stores without as if it’s second nature. They might discover a product on TikTok, compare prices on Google, check reviews on Reddit, and then complete their purchase in-store or through a mobile app. According to recent data, 36 % of Gen Z shoppers regularly use both online and in-person channels, making them the most omnichannel generation yet. For these shoppers, efficiency and authenticity are inseparable. They expect real-time updates and experiences they can trust, no matter where they shop.

For brands, this generation presents both a challenge and an opportunity. Gen Z demands transparency and speed. They are quick to abandon brands that deliver inconsistent information or clunky checkout experiences. To capture their attention, businesses must rethink omnichannel strategy not as a series of disconnected channels but as one continuous ecosystem — one that mirrors how young consumers actually evaluate and buy. That means ensuring every channel, from social commerce to brick-and-mortar, tells the same product story with the same level of clarity and appeal.

To achieve that level of consistency, brands need visibility into how their product experiences perform. This is where Akeneo PX Insights transforms complexity into insight. By measuring how product experiences perform across channels, PX Insights helps businesses understand where engagement thrives and where it falls short. It uncovers inconsistencies in descriptions or imagery, and pinpoints which content truly drives conversions among Gen Z audiences. Armed with these insights, brands can strengthen their omnichannel presence, ensuring every touchpoint feels trustworthy and true to the brand.

Turning Trends Into Opportunity

When you look at these trends together, they reveal one clear truth that experience transforms omnichannel strategy into a real opportunity. The future of retail is about weaving multiple channels into one seamless experience. Customers have made it clear: they want flexibility, reliability, and trust at every stage of their shopping journey. Brands that embrace a true omnichannel mindset, powered by accurate product data and AI-driven insights, will not only meet these expectations but turn them into lasting relationships.

In omnichannel retail, experience shouldn’t be an afterthought but the main priority. After all, 80% of consumers say the experience a company provides is just as important as its products or services, a clear reminder that experience is part of the product. When product information stays aligned across every touchpoint and the buying journey flows without barriers, what once felt fragmented turns into an opportunity. Every experience becomes a reason for customers to return.

It’s that time of year again; the leaves are falling, the air is crisp, and Akeneo’s Autumn Release is here to help your product experiences turn over a new leaf.

Just like nature’s most colorful season, this release is all about transformation. We’ve packed it with AI-powered enhancements, smarter workflows, and connected tools that make managing product information as smooth as your favorite pumpkin spice latte.

From intelligent asset management to AI-driven enrichment and seamless system connectivity, Akeneo’s Autumn Release is all about intelligent product data flow, intended to unite people, systems, and information across your entire tech stack. This release shifts the focus from simply architecting data models to setting data in motion, delivering smarter tools and connected workflows that propel product information effortlessly through your organization. 

As a result, your product data doesn’t just live within Akeneo. It moves fluidly to the teams, channels, and systems that need it most, empowering everyone with a single, reliable source of truth to make smarter decisions and craft more impactful product experiences.

With that in mind, let’s take a look at ten of the most exciting updates waiting for you inside Akeneo Product Cloud.

1. Intelligent Asset Management with Akeneo DAM

Say goodbye to messy file folders and manual tagging. The all-new Akeneo DAM revolutionizes product asset management by applying AI-driven categorization and sorting to your images. With automatic keyword tagging, teams can find the right product assets instantly, no matter the campaign, brand, or season. This means less time searching and more time creating engaging, conversion-ready experiences.

Fully integrated within Akeneo Product Cloud, this new DAM unites product information and product assets under one roof. Product, marketing, and eCommerce teams can collaborate seamlessly, ensuring consistency and quality across all channels. By connecting cutting-edge DAM functionality directly to Akeneo PIM, you’ll be able to unite product information and product assets in one powerful workflow that elevates your product experience strategy and accelerates conversions.

2. Centralized AI Configuration

The future of enrichment is not just AI – it’s transparent, unified, and scalable AI. With the Autumn Release, Akeneo introduces a new, centralized hub for all AI configurations, which allows teams to manage everything, including prompts, translation rules, and enrichment models, all in one place.

Now, users can preview AI-generated results in real time, test different prompts, and refine their configurations on the fly. This interactive setup builds trust in AI outputs, removes guesswork, and helps brands scale AI usage confidently across their entire product catalog.

No more scattered spreadsheets or one-off experiments. Just a single, transparent system where AI becomes a true co-pilot in your product data strategy.

3. Expanded AI-Powered Data Extraction

Your product assets tell powerful stories, and now, AI can help you tell them faster. Akeneo’s AI Extraction has expanded to generate product attributes and descriptions directly from media links, whether those assets live in a DAM, CMS, or other system.

This advancement transforms how teams work with multimedia content. Instead of re-entering data or relying on siloed systems, you can extract rich, descriptive product information automatically from videos, PDFs, or images, unlocking the full potential of your content library.

With AI handling the heavy lifting, your teams can focus on creativity and strategy, turning raw assets into compelling narratives that resonate with customers.

4. New & Improved Data Architect Agent

Announced in our Summer Release just a few months ago, the Data Architect Agent (DAA) was built to generate accurate and flexible data models in a matter of days, and now, it’s better and more powerful than ever.

The DAA now supports reference entities, asset families, attribute types, and variants, allowing organizations to co-create scalable, future-proof product data models with speed and precision. This enables teams to validate relationships between attributes, simplify onboarding for new products or categories, and minimize manual errors, ultimately helping your teams to design the most efficient structure possible and keep up with evolving business needs.

5. Smoother Collaboration Through Smarter Workflows

Teams need workflows that move as quickly as their data. That’s why the Autumn Release introduces a new generation of Akeneo Collaboration Workflows, now more connected, continuous, and intelligent than ever.

With Collaboration Workflow Connectivity, Akeneo extends its workflow capabilities beyond the PIM, opening the door for true cross-platform collaboration. Through newly introduced API endpoints, customers can now connect Akeneo workflows directly to external systems such as Slack, Jira, Asana, creative proofing tools, or even business intelligence dashboards. Teams can trigger notifications, manage tasks, and monitor progress across multiple platforms, all while maintaining Akeneo as the single source of truth.

But the innovation doesn’t stop at connectivity. Akeneo is also redefining how product workflows operate with Continuous Workflows. Whenever key updates occur, whether driven by AI enrichment, PX Insights, supplier inputs, or internal collaboration, Continuous Workflows automatically re-engage the right teams to validate and approve changes.

This shift from one-time validation to ongoing inspection ensures your product information remains accurate, complete, and consistent with business goals. Together, connected and continuous workflows form the backbone of a smarter, more adaptive product experience strategy—one where collaboration never stops and product data is always at its best.

6. Harmonized ERP Connectivity with SAP S/4HANA Integration

For enterprises running SAP S/4HANA, integration with Akeneo is now simpler and more powerful than ever. New integration packages streamline the flow of product data between systems, whether information is moving from S/4HANA to Akeneo or back again. By positioning Akeneo as the enrichment and activation layer on top of SAP, brands can easily localize, enrich, and validate ERP data for customer-facing channels, all while preserving data governance and minimizing the need for heavy ERP customization.

This enhanced connection delivers two major benefits. First, it reduces the total cost of ownership by offering a standardized integration blueprint built on the SAP Integration Suite (iPaaS), allowing teams to connect systems more quickly and with fewer maintenance challenges. Second, it accelerates time to market, enabling product data to flow effortlessly from ERP to PIM, where it can be enriched and activated across all customer touchpoints.

Together, SAP S/4HANA and Akeneo form a powerful, unified foundation that harmonizes operational efficiency with exceptional product experiences, bridging the gap between back-end systems and front-end storytelling to drive growth and agility.

7. Complete Control Over Enhanced Content

Rich, engaging content drives results, and with the new Akeneo Activation enhancements, you can turn Enhanced Content (also known as A+ Content) into a conversion catalyst.

Managing Enhanced Content across channels has long been a challenge, often requiring manual coordination and custom formatting. Now, Akeneo simplifies the process with an end-to-end workflow that unifies creation, governance, and distribution within the Akeneo ecosystem.

The result: teams can easily create immersive product stories that elevate brand perception, build customer confidence, and boost conversions by more than 20%.

Your entire product narrative, from core information to lifestyle imagery and storytelling elements, is now governed in one place, ensuring every detail is both accurate and inspiring.

8. Automated Catalog-to-Channel Mapping with AI

Mapping product data to channel requirements can be a time-consuming process. Each retailer, marketplace, and platform demands different formats and attribute structures. Akeneo’s AI-driven catalog mapping helps to minimize this bottleneck.

By intelligently matching your internal product attributes to each channel’s requirements, Akeneo drastically reduces setup time while improving accuracy. Teams can onboard new channels in record time, without the endless spreadsheets or manual configuration.

With this automation in place, your business gains agility to expand to new markets and platforms effortlessly, ensuring that your product information is always formatted perfectly for every channel.

9. Visibility into AI-Driven Search Engines with PX Insights

Visibility drives sales, but understanding how your products are found—and why they perform—is critical. With the Autumn Release, Akeneo PX Insights gets a major upgrade, adding new analytics, filters, and actionable intelligence.

By integrating visibility data from AI-driven search engines alongside traditional platforms like Google Shopping, PX Insights offers a holistic view of discoverability. You can see which products appear in search, how they rank, and what factors influence their visibility.

Even better, PX Insights now forms a feedback loop within Akeneo Product Cloud, helping you identify content gaps, enrich the right data, and measure performance instantly. This continuous improvement cycle ensures every enrichment effort directly strengthens your SEO, feed quality, and ad performance.

10. AI Models of Your Choice Integrated into Akeneo

The Akeneo App Store continues to evolve with the introduction of Akeneo MCP Server, a new environment designed for experimentation and innovation in product data intelligence.

This layer introduces early-stage capabilities that explore how AI and large language models (LLMs) can interact directly with product information. Within the “Bring Your Own LLM” playground, users can connect their preferred AI models to Akeneo, testing how AI might detect inconsistencies, surface recommendations, or enhance data governance.

By allowing AI agents to analyze and interact with product data, Akeneo takes the first step toward a self-improving product data ecosystem where machines help humans uncover insights faster and improve accuracy continuously.

It’s an exciting glimpse into the future of API-first, AI-empowered product experience management.

Turning Over a New Leaf in Product Experience

As the seasons change, so too does the way we create, manage, and share product experiences. Akeneo’s Autumn Release kicks off a new season of transformation, one powered by intelligent product flow that unites people, systems, and information across your entire tech stack. By setting data in motion, the team here at Akeneo is hyperfocused on helping your business grow stronger, smarter, and more connected than ever before.

Just as autumn brings clarity to the landscape, this release brings clarity and movement to your product data. With AI-powered enrichment, connected workflows, seamless integrations, and data that flows effortlessly between systems, the Akeneo Product Cloud ensures that every team has the information they need, where and when they need it. The result? A single, trustworthy source of product truth that fuels collaboration, speeds decision-making, and drives consistent, high-impact experiences across every channel.

So as you watch the leaves fall, remember: it’s the perfect moment to let outdated, disconnected processes drift away and make room for smarter, more fluid ways of working.

🍁 Ready to see what’s fresh this season?

Visit the Akeneo Autumn Release webpage to explore every new feature, or register for our live Deminar on December 3 to see how these innovations can help your product experiences flourish year-round.

If there’s one thing marketers have learned over the years, it’s this: never get too comfortable with Google.

Every few years, an algorithm update drops like a surprise pop quiz. Panda punished thin content, Penguin cracked down on spammy links, and Hummingbird made everyone suddenly care about “semantic search.” Each time, SEO pros scrambled to decode what it meant, rewrite their playbooks, and calm their clients.

But this time, it’s different.

The rise of AI-driven search and shopping isn’t just another algorithm tweak; it’s a full-blown revolution. Artificial intelligence is changing how people search, where they search, what they click (or don’t click), and how they buy. AI Overviews, large language models (LLMs), and new AI-driven shopping tools are reshaping the organic landscape faster than you can say “page rank.”

In short: Google’s updates used to shake up your keyword rankings.
Now, AI innovation is shaking up the entire idea of organic traffic.

The question every marketer is now asking: what happens when Google stops being your biggest traffic driver?

The Decline of Traditional Click-Through Experiences

If your organic traffic graph lately looks like a ski slope, you’re not alone.

According to SEMrush, AI-driven search could overtake traditional engines as the preferred search method by 2028. And we’re already seeing the ripple effects of this. Google’s AI Overviews, launched in 2024 as part of its Search Generative Experience (SGE), now appear in roughly 13% of queries, more than double what we saw from January to March 2025.

The result? Nearly 60% of all searches now end without a single click. Some industries are seeing traffic declines as steep as 15% to 64% since AI Overviews rolled out. Publishers, eCommerce sites, and SaaS companies are feeling it the hardest.
That means even when you’ve done everything “right”, optimized your content, targeted the perfect keywords, and snagged a top position, users might get their answer straight from Google’s shiny AI summary instead of visiting your site, and you’ll never reap the rewards.

In short: you’re still the brain behind the answer, but the robot gets the credit.

The Rise of LLM Search

If that all sounds a little bleak, here’s the silver lining: overall there are some signs suggesting that AI-referral traffic may be higher quality than traditional.

Visitors coming from LLMs like ChatGPT, Claude, or Gemini are proving to be high-intent power users. Data from SEMRush shows that LLM-driven visitors are 4.4 times more likely to convert than those arriving from traditional search.

Adobe’s 2024 holiday retail analysis backs this up: visitors from AI-driven search stayed 8% longer, viewed 12% more pages, and bounced 23% less than their Google-born counterparts.

And the growth? Astronomical. Between July 2024 and February 2025, AI-driven traffic to retail websites jumped 12x, outperforming traditional sources in engagement across the board.

Think of it this way: traditional SEO was about ranking. LLM search is about relevance. The AI doesn’t care about backlinks—it cares about useful answers. And if your brand delivers those, you’re in the game.

AI As a Sales Channel

Until recently, AI-powered interactions were all about discovery: product recommendations, personalized search results, and maybe a chatbot helping users find the right pair of sneakers. But now, artificial intelligence isn’t just influencing shopping decisions, it’s completing them.

Nearly a third of consumers have already used some kind of AI interface to make a purchase. Whether it’s asking a digital assistant for “the best noise-canceling headphones under $200,” or letting a smart mirror recommend a shade of lipstick, shoppers are starting to trust machines with their buying decisions. And the more intuitive these experiences get, the more that trust turns into transactions.

The biggest shake-up so far? ChatGPT’s instant checkout feature.
It’s still new, and the data’s early, but its potential is enormous. Imagine a shopper typing, “What’s a good moisturizer for sensitive skin?” and ChatGPT not only recommends your product, but lets them hit ‘Buy Now’ right there in the chat. No tabs, no scrolls, no bouncing between apps. Just a single, seamless conversation from curiosity to conversion.

For marketers, that’s both thrilling and a little terrifying. Traditional eCommerce funnels, the carefully crafted journeys from awareness to decision, are collapsing into a single AI-powered moment. Discovery, evaluation, and purchase now happen in one interface, guided by algorithms trained on millions of data points and conversations.

This shift builds an entirely new sales ecosystem, where the “storefront” might not even belong to you. The future of selling isn’t just about ranking high on Google or optimizing your PDPs. It’s about being present, trusted, and purchasable wherever the conversation happens, whether that’s on ChatGPT, Perplexity, Gemini, or whatever AI assistant your next customer decides to befriend.

How to Adapt to the Future of AI Commerce

If all this talk about AI eating your traffic and rewriting the sales funnel has you sweating—don’t. This isn’t the end of organic discovery; it’s just the start of a smarter, more dynamic version of it.

The brands that thrive in this new era won’t be the ones clinging to keyword rankings or chasing every algorithm tweak. They’ll be the ones structuring their data, refining their product stories, and meeting customers (and machines) exactly where they are.

Here’s how to future-proof your visibility and make sure your brand stays discoverable even when the next click never comes.

1. Understand how LLMs currently understand your business

Before you can optimize your visibility in AI-driven search, you first need to know what these models think you do. Large language models like ChatGPT, Gemini, and Claude are essentially well-read students; they’ve consumed massive amounts of information about your industry, your products, and your competitors. But whether they actually understand your brand correctly? That’s another story.

This is where tools like Akeneo’s AI Discovery Optimization feature come in, which helps businesses enrich, structure, and present product information in ways that align with how LLMs interpret and retrieve data. Instead of chasing the right keyword density or backlinks, the goal now is to make your content machine-comprehensible. The better an AI model understands your catalog, your values, and your differentiators, the more likely it is to include you in responses—or even recommend you outright.

Think of it as SEO for the AI age. Clarity, structure, and semantic richness replace keyword stuffing and meta tag tinkering. Your job isn’t to trick algorithms anymore—it’s to teach them who you are.

2. Incorporate customer feedback & insights into your product language

If you want to know how AI describes your brand, don’t look at your website – look at your customers. A recent study revealed that Reddit, Quora, and LinkedIn are among the most-cited sources in Google’s AI Overviews. Translation? AI is literally learning how to talk about your products from what real people are saying about them online.

The best way to stay relevant is to listen to those customer conversations and incorporate them into your product language. Use the same words, phrases, and problem statements your customers do. Reflect their tone and intent in your product descriptions, FAQs, and educational content. When your content mirrors authentic customer language, AI systems are more likely to cite you as a trusted, contextually relevant source.

3. Leverage structured and enriched data to increase AI discoverability

AI thrives on structured data. It’s how it decides what to trust. Use schema markup, comprehensive metadata, and enriched product attributes to make your content “machine-friendly.” The more context you give, the more likely AI systems are to understand (and cite) you accurately. When your product information is consistent, complete, and machine-readable, it’s far more likely to surface in AI Overviews, be cited in conversational answers, and even be recommended in emerging AI shopping tools.

4. Start tracking LLM referral traffic & other new KPIs to stay ahead

 As large language models like ChatGPT, Perplexity, Gemini, and Copilot start shaping more customer journeys, they’re quietly becoming meaningful sources of referral traffic. The catch? Most brands aren’t even tracking it yet. Start by looking for patterns in your analytics that hint at AI origins; unusual referral URLs, sudden spikes in direct traffic after trending topics, or visits that seem to appear out of thin air (spoiler: they probably came from an AI assistant). Some analytics platforms are already experimenting with dedicated tracking for AI-based referrals, and those who adopt early will gain a serious head start.

Beyond traffic, it’s time to expand your KPIs. Measure AI citation volume (how often your content is mentioned or quoted by AI tools), engagement depth (how long users stay after arriving from AI-powered sources), and AI-assisted conversions (purchases or sign-ups that began with an AI interaction).

Embracing the Future of Search & Shopping

Google may have been the sun around which your marketing universe once revolved, but that solar system is expanding fast. Now, discovery happens across an entire constellation of AI-driven platforms, assistants, and interfaces, all illuminating new pathways between curiosity and conversion.

Yes, that means the organic landscape is more complex than ever. But it’s also more exciting. For the first time, brands aren’t confined to search results. They can exist in conversations, recommendations, and even purchases that happen without a browser tab. The marketers who embrace this shift, who enrich their product data, listen to customer language, and understand how AI perceives their business, will be the ones who stay visible in the age of machine-mediated discovery.

Predicting the future is a tricky business. If we’d asked an expert in 2016 what shopping would look like today, they probably would’ve imagined drones dropping off Amazon boxes by the hour and smart fridges automatically restocking our oat milk.

Some of that’s happened (sort of), but the truth is, technology always surprises us, especially in eCommerce. Every year brings a new twist, and 2026 looks like it’s going to be one of the most fascinating yet.

That’s why we’re donning our futurist hats; not the shiny sci-fi kind, but the practical kind that marketers, retailers, and digital innovators wear when they try to spot what’s coming next. We’re analyzing real data, evolving consumer behavior, and the technologies already reshaping how we browse, buy, and engage in order to peer into the crystal ball and see what’s on the horizon. 

From AI-powered search tools that know your taste better than your best friend, to digital passports that tell the life story of your favorite products, here’s what’s about to redefine eCommerce as we head into 2026.

1. AI-powered product search & discovery

Traditional online search sometimes feels like shouting into the void. You type in a few keywords, cross your fingers, and hope the algorithm understands you. But for many shoppers, those days are over. The new era of eCommerce is powered by artificial intelligence that can actually infer and understand what you’re looking for, even when you don’t.

AI-driven discovery is already transforming how consumers browse, compare, and decide what to buy. 49% of Americans say AI recommendations already influence what they buy, and 64% of people say they’re willing to purchase items recommended by generative AI. SEMrush even predicts that AI-powered search will overtake traditional search as the preferred choice by 2028.

Visual search is also expected to keep gaining traction too, as consumers upload photos of something they saw on social media and find near-identical matches in seconds. Retailers are using these systems to personalize experiences in real time, learning from browsing behavior, preferences, and even mood signals to fine-tune recommendations.

2. The first step towards agentic commerce

We know—two AI trends in a row. But before your eyes glaze over, hear us out: if AI-driven search is about helping people find the right product, then large language model (LLM)-based purchasing is about helping them buy it without ever touching a checkout page.

LLMs like ChatGPT can now recommend products, compare features, and even complete purchases, all within a single chat. While this may sound futuristic, the adoption curve is already steep. A recent TechRadar report found that about one-third of U.S. consumers would let AI make purchases for them, and nearly a third have already used ChatGPT to assist in buying decisions.

Here’s how it might look in 2026: you tell your favorite AI assistant, “I need a gift for my dad—something under $100, and he loves gardening.” The assistant instantly pulls options from verified retailers, checks delivery dates, and finalizes the purchase, all while you sip your coffee. 

For brands, this opens a brand new sales channel. Of course, this shift also raises important questions about data privacy, consent, and how to maintain human connection when algorithms start handling transactions. But there’s no denying the momentum: AI may be helping us to shop today, but in the coming years, it will start to simply shop for us.

3. Increased supply chain transparency with Digital Product Passports

While AI tends to steal the headlines, another quietly revolutionary change is coming to eCommerce in 2026: the rise of the Digital Product Passport (DPP). Think of it as a digital birth certificate for every product, detailing where it was made, what it’s made of, and how it can be reused or recycled.

Driven by the European Union’s Ecodesign for Sustainable Products Regulation (ESPR), DPPs are set to become mandatory for certain product categories beginning in 2026 and 2027. These digital records will accompany items throughout their entire lifecycle, from raw materials to end-of-life, helping consumers make informed, sustainable choices.

For brands, this is both a compliance challenge and a storytelling opportunity. Consumers increasingly want to know the “why” behind what they buy; where it came from, who made it, and what impact it has. With DPP, a customer will be able to scan a QR code on a sweater and see its full history: which factory produced it, how much carbon it generated, and how to recycle or repair it later. 

By 2026, expect to see Digital Product Passports popping up across Europe and beyond. And once consumers get used to this level of visibility, there’s no going back.

See how Akeneo & Ocode can help you create Digital Product Passports.

4. Social commerce & user generated content (UGC)

Between 2023 and 2025, the number of social shoppers in the U.S. grew from roughly 96 million to 104 million. By 2026, those numbers will soar even higher, with TikTok alone projected to have nearly 40 million buyers in the U.S.

This growth isn’t surprising when you consider how people, especially younger audiences, discover products. Today, 43% of Gen Z consumers start their online shopping searches on TikTok, surpassing even Google and Amazon. Social media has become the new mall, where product discovery, community engagement, and purchasing all happen in one place.

User-generated content (UGC) also plays a massive role in this transformation. Shoppers trust people who look and talk like them more than polished brand campaigns. Reviews, unboxings, and “TikTok Made Me Buy It” videos are today’s word of mouth, and they convert at remarkable rates. Live shopping events, creator collaborations, and shoppable posts blur the line between entertainment and eCommerce, making shopping feel less transactional and more experiential.

By 2026, expect social commerce to become fully normalized. Platforms like Instagram, TikTok, and YouTube are continuing to refine in-app checkout options, allowing users to buy instantly without leaving their feeds. 

At the same time, brands are learning to co-create with their audiences, turning customers into advocates and content creators. The result? A more authentic, community-driven form of commerce, where a single viral video can sell out a product overnight.

5. A resurgence of hybrid shopping experiences

With all this talk about AI and digital transformation, you might think physical retail is dead. Spoiler alert: it’s not even close. In fact, hybrid shopping, where customers fluidly move between online and in-person touchpoints, has quickly become the norm.

Consumers still crave tangible experiences, and the data proves it. In a recent consumer survey we conducted here at Akeneo, we found that 75% of shoppers have bought something in-store after researching for it online, and 65% have done the opposite; purchasing a product online after browsing for it in a physical location.

In 2026, the most successful brands will be those that seamlessly blend the physical and digital worlds. Physical stores are already starting to evolve into experiential hubs: places to see, touch, and interact with products, to attend brand events, or even to process online returns with ease.

With so many advancements with the online shopping journey, it’s important to ask what’s driving this in-person surge? While consumers believe that algorithms can predict their preferences, they can’t fully replace the satisfaction of walking out of a store with your purchase in hand. For many consumers, that tactile joy will always matter. The smartest retailers aren’t choosing between physical and digital, but are designing ecosystems that make both shine.

How to Prepare for 2026 

Predicting the future of eCommerce may always involve a bit of educated guesswork, but one thing is certain: every major trend on the horizon depends on one thing above all else: great product data.

AI can’t recommend the right product if the underlying information is incomplete. Digital Product Passports can’t deliver transparency if product data is scattered across systems. Social commerce doesn’t work if your product descriptions, visuals, and attributes aren’t optimized for each channel. And hybrid experiences crumble without a unified, accurate source of truth about your products.

In 2026, success will belong to the brands that build a solid data foundation first. Reliable, enriched, and structured product information is what turns innovation into impact.

So while no one can perfectly predict what’s next in eCommerce, we can prepare for it. The future may be powered by AI and automation, but it runs on something far more fundamental: trustworthy product data. Get that right, and whatever the next big thing turns out to be, you’ll already be ahead of the curve.

Since the introduction of the world wide web, there have been a couple evolutions in eCommerce that stick out as key historical moments; Amazon breaking on to the scene in the late 90s to sell all products A to Z, the introduction of Cyber Monday to encourage online holiday shopping, and the eCommerce boom that came from quarantined consumers during the pandemic, to name a few.

Now it’s time to add a new one to the list: OpenAI’s new “Instant Checkout” feature.

For the first time, discovery, trust, and purchase can all happen seamlessly within a single conversation. There are no new tabs to open, no carts to abandon, no competitor websites to double check that you’re getting the best deal, and no friction between consideration and conversion. 

When the discovery channel itself becomes the point of purchase, the rules of engagement change entirely. For businesses that have long relied on traditional SEO strategies, paid digital ads, and organic traffic to attract customers, OpenAI’s latest announcement means that every dollar spent chasing success with Google’s everchanging algorithm, every hour spent optimizing metadata, and every campaign built around driving clicks to a website risks becoming wasted effort if that website is no longer where buying decisions happen.

The traditional pathways to purchase are being rewritten in real time, and if you aren’t prepared to ride this next wave of eCommerce evolution, you risk being left behind in the wake.

How to Prepare for OpenAI’s Instant Checkout

This is a new frontier of discovery and purchase, and as LLMs like ChatGPT change and evolve over time, we’ll gain a clearer understanding of how they interpret intent, prioritize responses, and decide which brands or products to surface within a conversation.

But what’s already clear is this: AI agents require contextualized, enriched, and accurate product information. Unlike traditional search engines that reward keyword optimization, these new systems rely on richly detailed and well-structured data to make meaningful connections between a shopper’s query and a brand’s offering. 

That means that success no longer depends on who can outsmart an algorithm, but on who can provide the most complete, consistent, and contextualized information within a data ecosystem that AI can actually understand and trust.

It also means that businesses need to be able to create a “closed loop” between product data and the LLMs themselves in order to ensure continuous refinement. In this loop, brands provide consistent, enriched, and contextualized data, and in return, they can learn how that data is being interpreted and presented to customers. 

Building that loop is just one piece of the puzzle. To truly prepare for this next wave of eCommerce, businesses will need to rethink how they manage, enrich, and connect their product information across every channel. 

That’s why the team here at Akeneo has identified five essential capabilities that all businesses need to embrace in order to ensure their product data is ready for agentic commerce.

1. Understand how you’re currently being interpreted

You can’t optimize what you don’t understand. If you don’t know how your products are being represented inside an LLM (or how your customers are actually talking about them), you’re effectively flying blind. 

PX Insights helps you see exactly how your products and brand are being talked about across AI-powered experiences and customer channels. By capturing real-life reviews, ratings, and feedback, you gain a complete picture of how your products are being discovered (or missed), and can then refine your data accordingly.  

This means that you’re not just optimizing for today’s models like ChatGPT, but for whatever is to come next. With PX Insights, you can learn from your product data, and close the loop between how your products are presented and how they actually perform.

 The good news? For existing Akeneo customers, PX Insights is available through a one-month free trial, offering a risk-free way to see how insights-driven optimization can transform your product experience strategy.

2. Ensure your product data is discoverable

Yesterday, being “ready” meant being SEO-compliant. Today, it means being GEO-compliant — optimized for Generative AI Engines. As we mentioned earlier, this means your product data needs to be rich enough, structured enough, and accurate enough for AI to understand and recommend it confidently.

Akeneo’s Data Architect Agent makes that possible by helping you design scalable, intelligent data models in minutes, drastically reducing the time and effort usually required to structure and standardize your product catalog. It ensures your data model isn’t just functional, but future-ready, giving your products the foundation they need to be discoverable across both traditional and AI-driven channels.

Ask yourself: do your products include every attribute a shopper needs to make a buying decision? Are your descriptions, specs, and images aligned with what customers actually search for and care about? Is your product data structured clearly enough for AI models to understand and surface it confidently? Are you able to adapt your product data in real time as customer preferences and LLMs evolve?

Only when your product data meets that level of completeness, clarity, and adaptability can your products be truly discoverable in this new era of agentic commerce.

It’s also important to point out that Shopify is already one of the first platforms compatible with ChatGPT’s latest feature. That means if your Shopify store is powered by Akeneo, you’re already in an incredibly strong position. Akeneo ensures your product data in Shopify is clean, complete, and consistent across every attribute, image, and description. When that enriched data flows into ChatGPT’s instant checkout experience, your products are surfaced accurately and compellingly, helping shoppers buy faster and with confidence. 

In short, by getting your product data right in Shopify — powered by Akeneo — you can optimize this powerful new AI-driven channel even faster.

3. Utilize the full power of your existing assets

Your product content is more powerful than you think. Assets like images, spec sheets, lifestyle videos, and installation guides are data-rich resources that can drive conversions when utilized effectively. But all too often, these assets sit idle, disconnected from the product experience they’re meant to enhance. 

With Akeneo, you can extract structured information from assets like PDFs or high-resolution imagery to automatically populate product attributes, saving time, reducing manual effort, and improving accuracy.

The result? Richer, more consistent product experiences that not only builds shopper confidence but also makes your data more legible to AI agents.

4. Tailor GenAI enrichment to your brand voice

Generic prompts create generic results. In a marketplace where differentiation is everything, your brand voice can’t be left to chance. Akeneo’s AI configuration panel gives you full control over how generative AI represents your brand, from tone and style to values and vocabulary.

You can design, test, and refine AI prompts that reflect your brand’s unique character, ensuring every product description, headline, or translation feels authentically you. With brand-aligned AI enrichment, you can deliver content that’s not only faster, but unmistakably on-brand.

5. Maintain human expertise in the loop

AI alone isn’t enough; the most successful brands know that automation should empower, not replace, their teams. With Akeneo’s built-in workflows, AI-generated content moves through human review and approval before publication, combining the efficiency of automation with the discernment of human judgment.

This approach safeguards accuracy, creativity, and compliance while dramatically shortening time to market. By keeping experts in the loop, you preserve brand integrity and product quality, turning AI into a trusted collaborator, not a black box.

Preparing for the Next Wave of eCommerce

Every major shift in eCommerce has redefined what it means to compete. From the dawn of online marketplaces to the rise of mobile shopping and social commerce, each transformation rewarded the brands that adapted fastest. 

OpenAI’s “Instant Checkout” marks the next evolution, where discovery and purchase no longer live on separate steps of the buyer journey but converge in a single, AI-driven moment of decision.

At Akeneo, we believe brands that thrive in the age of agentic commerce will be those that build stronger connections between their product information and the intelligent systems interpreting it. With tools like PX Insights, AI Discovery Optimization, and Data Architect Agent, you can close the loop between how your products are represented and how they’re discovered, ensuring your brand is not only visible today, but prepared for whatever comes next.

Not sure where to even begin? You can take our PX Maturity Assessment to give you a clear view of your strengths and the areas to prioritize first. 

Whether you’re launching your first PIM project or scaling a global ecosystem, we’re ready to meet you wherever you are. The future of commerce won’t wait, but readiness doesn’t mean rebuilding everything overnight. 

With Akeneo Product Cloud, you can turn product data into your strongest competitive advantage: complete, enriched, consistent, and ready to shine in every new channel, including ChatGPT. The rules of commerce may be changing, but with the right foundation, your brand can not only keep up, but lead.

Do you remember the first time your favorite local coffee shop greeted you by name and started making your usual order before you even asked? That moment felt like exceptional service—but really, it was your first taste of a truly personalized experience.

Today, that same expectation extends far beyond your morning coffee. Consumers don’t want generic messages or one-size-fits-all offers; they expect brands to understand their preferences and anticipate their needs. In fact, our survey found that two in five consumers would pay up to 25% more for a personalized shopping experience.

What Does “Personalized Experience” Mean?

A personalized experience is any brand interaction that feels intentionally made for an individual customer — based on who they are, what they do, and what they need. Rather than pushing the same products or messaging to everyone, these experiences adjust in real time using customer insights like past purchases, browsing habits, location, or even customer service interactions. This kind of authentic connection makes customers feel recognized and helps deliver a high-quality product experience that builds trust from discovery to post-purchase.

Different Types Of Personalized Experiences

Personalization takes many forms. And each plays a unique role in shaping how individual customers engage with your brand:

1. Product Recommendations: Helping Customers Find What They Didn’t Know They Needed

Product recommendations are one of the most recognizable and impactful forms of personalized customer experience. Whether it’s the classic “Customers also bought” row or a curated homepage based on your browsing behavior, these dynamic suggestions help guide customers toward relevant products and services they may not have discovered otherwise. 

What makes this approach so powerful is its ability to connect customers with offerings that align with their tastes and needs, without having them do the searching themselves. This not only improves product discovery and shortens the path to purchase, but also increases basket size and satisfaction, making customers feel like the brand gets them. For many businesses, product recommendations are a major driver of both loyalty and margin, turning a basic browsing session into a high-value customer interaction rooted in personalization.

How AI Powers This:

Artificial Intelligence (AI) and Machine Learning (ML) sit at the core of modern recommendation engines. These technologies analyze a combination of real-time customer behavior (such as time spent on pages and cart activity) and historical data like past purchases, product ratings, or even abandoned items. By continuously learning from this data, AI models can predict what an individual customer is likely to want next and surface it instantly, whether that’s through the homepage or a Product Detail Page (PDP).

And AI is doing more than just improving the checkout experience; 49% of shoppers say AI-powered recommendations have influenced their purchase decisions. And with ChatGPT’s recent announcement of their “Instant Checkout” feature that allows consumers to purchase products directly within the AI interface, we can only expect this percentage to grow.

2. Personalized Messaging: Speaking to the Right Customer at the Right Time

Forget mass email campaigns! Today’s customers expect messaging that’s timely and customized to their individual preferences. Personalized messaging, whether through email, SMS, push notifications, or social media, enables brands to speak directly to each customer with content that reflects their interests and position in the purchase journey. From abandoned cart nudges to restock alerts, every touchpoint becomes an opportunity to deepen engagement and drive retention. When personalization efforts are thoughtful and well-timed, they feel like a natural part of the shopping experience. And as customers navigate an expanding landscape of channels and interactions, contextual messaging is essential for delivering a unified and seamless brand experience.

How AI Powers This:

Delivering that level of personalization across multiple touchpoints isn’t easy, especially at scale. That’s where AI makes the difference, helping brands move beyond static segments to build dynamic messaging strategies that respond to customer behavior. With the ability to generate tailored content and adapt on the fly, AI ensures communication stays relevant and contextual. Tools like Akeneo PX Insights play a key role in making this possible by leveraging behavior data to inform smarter decisions within your Product Information Management (PIM). The solution empowers teams to optimize messaging and product content based on how customers actually interact across channels. And by uncovering not just what customers engage with, but why, brands can deliver smarter product experiences grounded in real insights.

AI’s role in performance optimization becomes even more impactful when applied to customer communication. Take Amazon, for example — their AI-powered personalization strategy has transformed how they engage customers through email. By analyzing individual behavior, purchase history, and preferences, Amazon’s machine learning algorithms generate hyper-personalized email content tailored to each recipient. Suppose a customer recently purchased a book by a particular author. In that case, they might receive a follow-up email recommending other titles by the same or similar authors, all automatically curated by AI. This level of intelligent automation drives higher open rates and helps brands maintain consistent, high-value interactions over time.

3. Intuitive Browsing: Making Discovery Feel Effortless

One of the most underrated forms of personalization is how a customer navigates your digital storefront. Intuitive browsing is about making it easy (even enjoyable!) for customers to find what they’re looking for, or better yet, stumble across something they didn’t know they needed. This involves more than good UX design; it’s about adapting the interface, filters, categories, and search results to match the behavior and preferences of each customer. 

A returning visitor might see their favorite categories featured prominently, while a new shopper might be guided through a curated onboarding experience. The goal is to remove friction from the exploration phase of the customer journey and turn it into a satisfying experience that keeps people engaged and encourages repeat visits.

How AI Powers This:

This same intelligence extends beyond messaging, shaping how customers explore and discover products. AI plays a key role in making digital browsing feel more intuitive by adapting interfaces in real time based on user behavior and context. Rather than relying on fixed categories or keyword-matching alone, AI-powered systems use ML and Natural language processing (NLP) to interpret what a customer really means — not just what they type.

Take a search for “lightweight winter jacket.” A traditional search engine might simply pull every item containing those keywords in the title or description. In contrast, AI can analyze customer reviews to identify jackets most often praised for warmth without bulk, recognize which styles perform best in colder climates, and even factor in recent weather trends to promote products that align with real conditions. This level of intelligence helps deliver results that match intent instead of just wording, offering more meaningful and accurate recommendations.

Beyond search results, AI also reshapes the broader browsing experience, adjusting navigation menus and reorganizing category layouts to reflect real-time preferences. Brands like Sephora are already leading the way: after noticing a rise in detailed product searches, they used AI to generate landing pages tailored to queries like “best foundation for sensitive skin.” This not only delivered hyper-relevant content to curious shoppers but also led to a 6% boost in organic search visibility. It’s a perfect example of how AI can power both a smoother browsing journey and smarter product discovery, all while driving measurable results.

Personalization Starts with the Right Foundation

Great personalized experiences can only exist because of great data. No matter how sophisticated your AI or how creative your messaging, if the underlying product information is inconsistent or outdated, the experience falls flat. Today’s customers expect detailed product content, and when they get it, they’re more likely to convert and stay satisfied. In fact, 62% of consumers say they’re far more likely to keep a product and feel good about their purchase when the information they received was comprehensive and correct. 

Conversely, poor data drives up return rates and eats into margins. And in 2025, nearly a third of consumers reported dissatisfaction with the depth of product information they received, highlighting a growing gap between expectations and reality.

That’s why investing in tools like a PIM is essential. A modern PIM helps centralize product content, ensure consistency across all channels, and scale personalization efforts with confidence. Paired with enrichment capabilities powered by AI, brands can go beyond accuracy and start tailoring experiences for individual shoppers — delivering the right message, with the right information, at exactly the right moment. 

And the stakes are high: 77% of shoppers say they would abandon a site and look elsewhere if product details, pricing, or specifications were incomplete. This kind of content-driven personalization not only improves conversions but also reduces costly returns and preserves customer loyalty. 

The Power of Getting Personal — with Precision

Personalized experiences are grounded in great product data. Without complete, consistent, and context-rich information, even the smartest AI can’t deliver relevance or drive the kind of engagement today’s customers expect! From search to messaging to browsing, every touchpoint depends on the accuracy and depth of your product content.

When the product experience matches the product promise, everyone wins. With the right data foundation in place, supported by tools like PIM and enriched by AI, personalization becomes the best thing a business could have:  a sustainable growth strategy.